class_docstr: Tests the correctness of the Y = g(X) = Log[1 + exp(X)] transformation.
code: def testDtype(self):
      b = softplus.Softplus()
      self.assertIsNone(b.dtype)
  
      b = softplus.Softplus(low=1.75)
      self.assertEqual(tf.float32, b.dtype)
  
      b = softplus.Softplus(
          hinge_softness=tf.constant(0.5, dtype=tf.float64)
      )
      self.assertEqual(tf.float64, b.dtype)
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector_test_util
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 19 for calling the SoftplusBijectorTest obj's underlying member method code with example usage: obj.testDtype() without return types